Skip connections have been widely used in popular convolutional networks including U-Net[1]}, ResNet[2]}, etc. Inspired by [3]}, we propose to integrate the attention maps to the skip connections which connect the network encoder and the multi-class decoder. In the skip connections, we also introduce an extra residual path to recover the vanilla feature maps and further improve the segmentation performance. Compared with traditional residual paths, the magnitude factor of the residual path \(\alpha _{i}, i \in (1, 2, ..., 5)\)  is set as a learnable parameter of the network, which is updated during the back propagation. We believe such settings could add extra non-linearity capacity of the network and enhance the effectiveness of skip connections.
